code: def get_predictions(olist, batch_size):
      bboxlists = []
      variances = [0.1, 0.2]
      for i in range(len(olist) // 2):
          ocls, oreg = olist[i * 2], olist[i * 2 + 1]
          stride = 2 ** (i + 2)  # 4,8,16,32,64,128
          poss = zip(*np.where(ocls[:, 1, :, :] > 0.05))
          for Iindex, hindex, windex in poss:
              axc, ayc = (
                  stride / 2 + windex * stride,
                  stride / 2 + hindex * stride,
              )
              priors = np.array(
                  [
                      [
                          axc / 1.0,
                          ayc / 1.0,
                          stride * 4 / 1.0,
                          stride * 4 / 1.0,
                      ]
                  ]
              )
              score = ocls[:, 1, hindex, windex][:, None]
              loc = oreg[:, :, hindex, windex].copy()
              boxes = decode(loc, priors, variances)
              bboxlists.append(np.concatenate((boxes, score), axis=1))
  
      if len(bboxlists) == 0:  # No candidates within given threshold
          bboxlists = np.array([[] for _ in range(batch_size)])
      else:
          bboxlists = np.stack(bboxlists, axis=1)
      return bboxlists
  
file_path: face-alignment/face_alignment/detection/sfd/detect.py
imports: import torch
  import torch.nn.functional
  import cv2
  import numpy
  from .bbox import *
label_desc: Use image node_id 3 for calling a global function with example usage: get_predictions(olist, batch_size) and returns: bboxlists